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Malaria incidence (MI) has significantly declined in Nepal, and this study aimed to investigate the spatiotemporal distribution and drivers of MI at the ward level. Data for malaria cases were obtained from the National Surveillance System from 2013 to 2021. Data for covariates, including annual mean temperature, annual mean precipitation, and distance to the nearest city, were obtained from publicly available sources. A Bayesian spatial model was used to identify factors associated with the spatial distribution of MI.
In high-transmission areas, school-aged children have higher malaria prevalence and contribute significantly to the transmission reservoir. Malaria infections can be asymptomatic or present with symptoms which may contribute to anaemia, severe illness and fatal malaria. This analysis provides trends of malaria prevalence and associated risk factors among school-aged children in mainland Tanzania.
The World Health Organization recommends perennial malaria chemoprevention (PMC), generally using sulfadoxine-pyrimethamine (SP) to children at high risk of severe Plasmodium falciparum malaria. Currently, PMC is given up to age two in perennial transmission settings. However, no recommendation exists for perennial settings with seasonal variation in transmission intensity, recently categorized as 'sub-perennial'.
New malaria vaccine development builds on groundbreaking recommendations and roll-out of two approved pre-erythrocytic vaccines (PEVs); RTS,S/AS01 and R21/Matrix-M. Whilst these vaccines are effective in reducing childhood malaria within yearly routine immunization programs or seasonal vaccination, there is little evidence on how different PEV efficacies, durations of protection, and spacing between doses influence the potential to avert uncomplicated and severe childhood malaria.
Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control.
The most recent global estimates of the number of pregnancies at risk of Plasmodium falciparum and Plasmodium vivax malaria infection are from 2007. To inform global malaria prevention and control efforts, we aimed to estimate the global distribution of pregnancies at risk of malaria infection from 2007 to 2020.
Children recovering from severe malarial anaemia (SMA) remain at high risk of readmission and death after discharge from hospital. However, a recent trial found that post-discharge malaria chemoprevention (PDMC) with dihydroartemisinin-piperaquine reduces this risk. We developed a mathematical model describing the daily incidence of uncomplicated and severe malaria requiring readmission among 0-5-year old children after hospitalised SMA.
The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies.
HIV, tuberculosis (TB) and malaria are the three most important infectious diseases in Ethiopia, and sub-Saharan Africa. Understanding the spatial codistribution of these diseases is critical for designing geographically targeted and integrated disease control programmes. This study investigated the spatial overlap and drivers of HIV, TB and malaria prevalence in Ethiopia.
As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low-prevalence areas are increasingly needed. For low-burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons.